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This study aims to examine the effects of multidimensional factors of platform features, group effects and emotional attitudes on social media users’ privacy disclosure intention.

This study collected the data from 426 respondents through an online questionnaire survey and conducted two approaches of structural equation modeling (SEM) and fuzzy-set qualitative comparative analysis (fsQCA) for theoretical hypothesis testing and configuration analysis of the data.

The results show that social media platform features (rewards of information disclosure, personalized service quality and data transparency), group effects (group similarity, group information interaction and network externality), individual emotional attitudes (trust and privacy concern) and control variable (gender) have a significant impact on privacy disclosure intention, as well as trust and privacy concern play mediating roles. Additionally, the fsQCA method reveals five causal configurations that explain high privacy disclosure intentions. Furthermore, the study reveals that male users pay more attention to platform features, while female users are more inclined to group effects.

This study attempts to construct a comprehensive model to examine the factors that affect users' intention to disclose their privacy on social media platforms. Drawing on the cognition-affect-conation model and multidimensional development theory, the model integrates multidimensional factors of platform features, group effects, trust and privacy concern to complement existing theoretical frameworks and privacy disclosure literature. By understanding the complex dynamics behind privacy disclosure, this study helps platform providers and policymakers develop effective strategies to ensure the vitality and momentum of the social media ecosystem.

Research and Development (R&D) activities are important for technological innovation and present opportunities for entrepreneurship. These activities depend on the flow of funding. This paper aims to review approaches used in R&D project selection and budget allocation.

This study conducts a systematic review, examining the content of 60 relevant papers (spanning 2000–2022) concerning public R&D budget allocation. The analysis focuses on allocation methodology, R&D output evaluation, budget allocation efficiency and the management of uncertainty in the allocation process.

The systematic review reveals different methods proposed for allocating government R&D budgets. These methods range from classical optimization, multi-criteria analysis and hierarchical analysis to techniques such as balanced scorecard, data envelopment analysis and analytic hierarchy process, including fuzzy approaches. Recent trends indicate an increase in the use of advanced optimization, integration and simulation algorithms. Performance indicators for reflecting R&D project outputs or goals can be categorized into four main groups: output (e.g. publications, patents, graduates), outcome, productivity (e.g. citations, patent references, articles and patents per capita) and sector-specific metrics.

Future research directions in government R&D budget allocation may include optimizing allocation to maximize social, economic and political benefits, developing ranking models, decision-making frameworks, simulations and evaluations of factors influencing allocation type and strategy. Additionally, there is a growing interest in novel budget allocation algorithms leveraging artificial intelligence and self-adjusting meta-heuristic algorithms.

The systematic review showed that some important research gaps in (government) R&D budget allocation could be considered in future studies; for example, long-term social, economic and political benefits in budget allocation optimization models, comprehensiveness of allocating government R&D budgets to universities, higher education and research institutes, R&D budget allocation to strategic technology development, e.g. renewable energy sector, supply chain issues and renewable energy value chain; new budget allocation algorithms based on artificial intelligence and self-adjusting meta-heuristic algorithms; methods for optimizing the structures of government budget allocation to R&D, considering executive and regulatory conflicts.

In the context of green development in China, the circumstance in which Environmental, Social and Governance (ESG) ratings function has changed. As an important external governance mechanism of sustainable development, ESG ratings can also be a two-edged sword for the implementation of carbon emission reduction. This research examines the connection of ESG ratings and corporate carbon emission reduction in the context of green development. This present study postulates that the impact of ESG ratings on carbon emission reduction performance in the context of green development is inverted U-shaped.

To obtain empirical evidence for the hypotheses proposed, this study makes an empirical test based on the two-way fixed effects model. The data is taken from listed Chinese manufacturing firms between 2012 and 2021.

The study reveals that there is a significant inverted U-shape relationship between ESG ratings and carbon emission reduction performance in the context of green development. Managerial myopic behaviour plays a positive moderating role in the above relationship. In addition, it makes the inflection point of inverted U-shaped curve move to left. Heterogeneity analyses show that the above inverted U-shaped relationship is more significant for firms that don’t hire CEO with environmental protection background or big four accounting firms.

In the background of green development, this study helps to understand dual influence of ESG ratings on corporate carbon emission reduction deeply. It is beneficial to guide enterprises to utilize ESG ratings mechanism reasonably, thus enhancing the effectiveness of carbon emission reduction. This study provides decision-making reference for government to accelerate low-carbon transformation in microcosmic field.

The emergence of live streaming commerce has injected promising impetus into rural development and attracted many rural streamers. This study aims to explore the influencing factors of rural streamers’ engagement intentions to help promote the sustainable development of rural live streaming commerce.

Grounded in the extended valence framework, this research employs a mixed-methods approach encompassing both qualitative and quantitative methodologies. In the qualitative phase, the authors conduct in-depth interviews with 15 rural streamers, employing data coding techniques to uncover underlying factors. Subsequently, in the quantitative phase, the authors analyze survey data from 282 rural streamers, subjecting hypotheses to validation through structural equation modeling.

The findings derived from the analysis of both interviews and questionnaires reveal that several platform qualities, including platform rural-aiding support, perceived effectiveness of dispute resolution, perceived interactivity and platform reputation, have a positive effect on trust in the platform and validate the extended valence framework in understanding rural streamers’ live streaming intention. In addition, ties with customers have a moderating effect. Specifically, the stronger the ties with customers, the stronger the positive effect of perceived benefits and the weaker the positive effect of trust in the platform on live streaming intention will be.

This study contributes to the rural live streaming commerce literature and trust research from the sellers’ perspective and provides practical implications for policymakers and live streaming platform managers on enhancing rural streamers’ participation.

Over recent years, public health threats, economic losses and crises of confidence resulting from adverse events along the global food chains have pressured food traceability systems to operate more efficiently. Based on that, this paper aims to expand the body of knowledge on the connection between Industry 4.0 technologies and food chain traceability and how they have been integrated.

Before conducting a systematic literature review (SLR), we consulted academic experts in traceability and Industry 4.0 technologies to define the leading 4.0 technologies adopted. We then developed protocols and criteria for article selection. Based on the developed codebook, we performed a content analysis, refining it through discussions with experienced researchers.

This paper identified seven Industry 4.0 technologies applied in conjunction with support and device systems. These combinations hold promise for generating value for food chain traceability systems. Value for the food supply chain originates from five sources: trust, process improvement, data processing, security, sustainability and regulation. Besides, three barriers to implementing such traceability technologies were identified: resources, lack of structure, stakeholders and values.

While this study focused specifically on the food chain, the technology combinations, values, barriers and categories identified herein can inform analyses for other chains, e.g. the pharmaceutical chain. Moreover, due to the recent growth of this topic, some existing values and barriers may not have been fully explored and the technology combinations may vary slightly.

The results of this study provide food chain professionals with a comprehensive understanding of the values and corresponding barriers associated with employing Industry 4.0 traceability technologies. This knowledge can be leveraged to develop effective strategies and implement improvements in traceability, thereby benefiting society and addressing identified barriers.

This study expands the literature on using Industry 4.0 technologies for traceability in food chains, providing valuable directions to build safer, more efficient, transparent and potentially more sustainable food chains. Additionally, we provide promising avenues for future research.

In the context of growing efforts by online businesses to enhance consumer connections, understanding consumer engagement behaviors is imperative. This study explores consumer engagement within online shopping platforms, specifically introducing and examining the roles of crowdsourcing and crowdsending.

The study developed and validated measurement scales for crowdsourcing and crowdsending engagement across transactional, multi-sided and inspirational platforms.

Identifying five sub-dimensions within crowdsourcing and crowdsending, the results unveiled nuances in consumer–platform interactions, emphasizing the value of co-creation. Crowdsourcing entails transaction-oriented engagements such as knowledge gathering, utilitarian and hedonic browsing, interaction and co-shopping. The findings revealed that crowdsourcing significantly influenced platform commitment, surpassing the impact of crowdsending on transactional platforms. Conversely, crowdsending involves knowledge sharing, feedback, participation, advocacy and reciprocity, fostering active engagement and shared value within the platform ecosystem. Notably, the results showed that crowdsending strengthened commitment to inspirational platforms more than to conventional shopping platforms.

This study contributes to the theoretical understanding of a range of consumer engagement experiences in online shopping environments and presents practical applications, offering valuable insights for commerce businesses aiming to optimize their digital strategies.

To overcome the shortcomings of traditional dynamic parameter identification methods in accuracy and efficiency, this paper aims to propose a dynamic parameter identification method based on improved iterative reweighted least squares (IIRLS) algorithm.

First, Newton–Euler method is used to establish the dynamic model of the robot, which is linearized and reorganized. Then, taking the improved Fourier series as the excitation trajectory, the optimization model with objective function is established and optimized. Then, the manipulator runs the optimized trajectory and collects the running state of the joint. Finally, the iterative process of iterative reweighted least squares (IRLS) algorithm is improved by combining clustering algorithm and matrix inversion operation rules, and the dynamic model of robot is identified by using the processed collected data.

Experimental results show that, compared with the IRLS algorithm, the root mean square of the proposed IIRLS algorithm is reduced by 4.18% and the identification time is reduced by 94.92% when the sampling point is 1001. This shows that IIRLS algorithm can identify the dynamic model more accurately and efficiently.

It effectively solves the problem of low accuracy and efficiency of parameter identification in robot dynamic model and can be used as an effective method for parameter estimation of robot dynamic model, which is of great significance to the research of control method based on robot model.

Shopping through e-commerce platforms has become a primary daily activity. However, research on consumer engagement within e-commerce platform contexts remains scarce. We examine the relationship between consumer engagement on online shopping platforms and their subjective well-being, considering self-expansion and self-extension as mediators.

We investigate the role of consumer engagement by dividing it into two experiences (crowdsourcing and crowdsending). Using validated measurement scales to analyze data from 440 South Korean consumers, we examine how these engagement experiences affect self-expansion and self-extension, ultimately leading to higher subjective well-being.

Crowdsourcing and crowdsending play different and complementary roles in improving self-concept. Furthermore, self-expansion and self-extension are key variables influencing consumer engagement and well-being on the platform.

This study provides a new perspective of consumer online shopping behavior, revealing the self-related mechanisms that influence the relationship between consumer engagement experiences and subjective well-being.

This aim of the research is to explore the link between offline and online retail sales through a comprehensive analysis of 15 years of retail sales data in Great Britain. By quantifying the impact of online sales on offline sales and vice versa on different retail categories, the study seeks to expand upon prior research that often focused on unidirectional relationships or specific retail segments. With channel- and sector-specific dynamics, the present study aspires to shed light on the dynamics shaping the retail industry and provide valuable insights for retailers.

The present study draws on 15 years of retail sales data in Great Britain to examine the interplay between offline and online sales across various retail categories. By grounding the analysis in the theoretical underpinning of cross-channel synergies and dis-synergies and applying principles of substitution and complementarity in online and offline retail sales, the research aims to quantify how shifts in online retail influence offline sales and vice versa. The study follows a systematic analysis approach that includes analysis of seasonality, autocorrelation, correlation, lagged cross-correlation and time series regression to provide a holistic understanding of the strength and direction of the relationship.

Among the myriads of findings, the most noteworthy ones are these. It was found that offline sales tend to increase significantly when online sales increase and online sales also increase significantly when offline sales increase. However, the extent of influence offline sales have on online sales is lower than the extent of influence online sales have on offline sales. For instance, it was found that for every one-unit increase in online sales, on average, the offline sales of retail stores increase by 5.82 units, but for every one-unit increase in offline sales, on average, the online sales of retail stores increase only by 0.07 units. This pattern was found true for all categories for retail stores, with predominantly non-food stores showing the lowest increase (0.02) and non-store retails exhibiting the highest increase (0.23). Additionally, evidence suggested that the retail stores can expect a sizable level of offline sales when there are no online sales (β₀ = 5155474.1); however, this was not true for online sales if they have no offline sales.

The study deepens our insight into the complex interplay between offline and online sales in the retail sector by providing empirical evidence on how online sales and offline sales interact with each other in a retail setting. It also adds nuances to existing knowledge on the relationship between online and offline sales based on channel and sector-specific dynamics. Such findings, coming from the analysis of a whole retail economy, strengthen the existing body of literature and provide a solid foundation for future research in this area. However, the study would have benefited from including qualitative interviews of different categories of retailers to provide more context and depth on interpreting quantitative patterns.

The study provides practical guidance for retailers on how they can optimize their omnichannel strategies for maximum impact. It also provides insights into the changes in relationship in terms of channel-specific and sector-specific differences. This is particularly important for retailers who operate in those categories of retail as it provides them with an insight into how they can allocate their resources for reaping substantial benefits.

Despite the wealth of research exploring the link between online and offline retail sales, there remains a gap in understanding the complex relationship between these two. This study is one of the first to fill this gap by exploring the link through a comprehensive analysis of 15 years of retail sales data in Great Britain. With these channel- and sector-specific findings, this study adds to the growing body of literature that provides an understanding of the bidirectional relationships between offline and online sales. These strengths strengthen the existing body of literature and provide a solid foundation for future research in this area.

The purpose of this study is to evaluate the tea stem natural dye was extracted from tea stem waste and applied to dyeing silk fiber, after which the properties of dyed samples were tested and analyzed.

The dyeing process was optimized using the response surface methodology (RSM) approach. Dyeing temperature, pH and time were chosen as variables and the color difference value as a response. The properties of dyed samples were tested and analyzed.

The optimized dyeing process was as follows: dyeing temperature 70°C, pH 3.5 and time 110 min. The K/S and color difference value of silk fiber dyed with the optimal process dye enzymatic oxidation with laccase was 1.4 and 27.8, respectively. The silk fiber dyed has excellent color fastness, antioxidant and antibacterial property, which greatly increases the added value of the dyed products. Furthermore, the optimized dyeing process did not significantly affect the strength properties and handle of the silk fiber.

Researchers have not used statistical analysis to optimize the process of dyeing process of silk fiber by tea stem natural dye enzymatic oxidation with laccase using response surface methodology. Additionally, this dyeing process was a low-temperature dyeing process, which not only saves energy consumption and reduces silk fiber damage but also obtains superbly dyeing results and biological functional properties, achieve the effects of waste utilization and clean dyeing.